class_docstr: Policy that derives its actions from a learned Q function.
  
  The action returned is the one that yields the highest Q value for
  a given state, as determined by the supplied Q function.
  
  Args:
      qf (object): Q network.
      env_spec (EnvSpec): Environment specification.
      name (str): Name of this policy.
docstr: Get actions given observations.
  
  Args:
      observations (np.ndarray): Batch of observations, should
          have shape :math:`(N, O)`.
  
  Returns:
      torch.Tensor: Predicted actions. Tensor has shape :math:`(N, A)`.
      dict: Empty since this policy does not produce a distribution.
docstr_tok: ["Get","actions","given","observations",".","Args",":","observations","(","np.ndarray",")",":","Batch","of","observations",",","should","have","shape",":","math",":","`","(","N",",","O",")","`",".","Returns",":","torch.Tensor",":","Predicted","actions",".","Tensor","has","shape",":","math",":","`","(","N",",","A",")","`",".","dict",":","Empty","since","this","policy","does","not","produce","a","distribution","."]
code: def get_actions(self, observations):
      """Get actions given observations.
  
      Args:
          observations (np.ndarray): Batch of observations, should
              have shape :math:`(N, O)`.
  
      Returns:
          torch.Tensor: Predicted actions. Tensor has shape :math:`(N, A)`.
          dict: Empty since this policy does not produce a distribution.
      """
      with torch.no_grad():
          return self(np_to_torch(observations)).cpu().numpy(), dict()
  
file_path: garage/src/garage/torch/policies/discrete_qf_argmax_policy.py
imports: import numpy
  import torch
  from garage.torch import np_to_torch
  from garage.torch.policies.policy import Policy
label_desc: Use image node_id 4 for calling the DiscreteQFArgmaxPolicy obj's underlying member method code with example usage: obj.get_actions(observations) and returns: self,  dict